yaImpute: AnRPackage forkNN Imputation

Crookston, Nicholas L.; Finley, Andrew O.

doi:10.18637/jss.v023.i10

Cited by 326 publications

(254 citation statements)

References 18 publications

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“…Before a back transformation of the natural log biomass estimate was performed, a bias-correction factor of 0.5 times the mean square error was added to the estimates (Baskerville, 1972;Goerndt et al, 2010). Most similar neighbor (MSN), gradient nearest neighbor (GNN), k-nearest neighbor (k-MSN), and random forest (RF) were performed using the yai and impute tools within the yaImpute (Crookston & Finley, 2008) R-package.…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Gagliasso¹,

Hummel²,

Temesgen³

2014

OJF

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Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future management plans. The goal of this study was to compare various imputation methods to predict forest biomass and basal area, at a project planning scale (<20,000 acres) on the Malheur National Forest, located in eastern Oregon, USA. We examined the predictive performance of linear regression, geographic weighted regression (GWR), gradient nearest neighbor (GNN), most similar neighbor (MSN), random forest imputation, and k-nearest neighbor (k-nn) to estimate biomass (tons/acre) and basal area (sq. feet per acre) across 19,000 acres on the Malheur National Forest. To test the different methods, a combination of ground inventory plots, light detection and ranging (LiDAR) data, satellite imagery, and climate data was analyzed, and their root mean square error (RMSE) and bias were calculated. Results indicate that for biomass prediction, the k-nn (k = 5) had the lowest RMSE and least amount of bias. The second most accurate method consisted of the k-nn (k = 3), followed by the GWR model, and the random forest imputation. For basal area prediction, the GWR model had the lowest RMSE and least amount of bias. The second most accurate method was k-nn (k = 5), followed by k-nn (k = 3), and the random forest method. For both metrics, the GNN method was the least accurate based on the ranking of RMSE and bias.

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Section: Discussionmentioning

confidence: 99%

“…The k-MSN method uses the same methods as MSN, but takes an average of the k nearest neighbor of plots. The Random Forest (RF) imputation method creates a classification matrix and regression tree in order to find similarities between the explanatory and response variables (Crookston & Finley, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Gagliasso¹,

Hummel²,

Temesgen³

2014

OJF

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“…This votes matrix can be scaled and treated as a probability given the error distribution of the model. We used the function that (Evans and Cushman 2009) added to GridAsciiPredict (Crookston and Finley 2008) which uses the votes-probability function to write the probabilities to ASCII grid(s). Model predictions for the logistic regression model were created by calculating p = exp(z)/(1 ?…”

Section: Modelling Approachesmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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“…The R package yaImpute [34] was used identify the nearest neighbour (k = 1) in the reference dataset which was then used to impute the array of BA by 2 cm Dbh class, ranging from Dbh9, Dbh11, …, Dbh69 where Dbhi is the Dbh class (i -1) ≤ Dbh < (i + 1). The function "yai" was used with method = "randomForest" and the supplied defaults, including the number of regression trees = 500 and mtry (the number of predictor variables picked a random) equal to the square root of the number of predictor variables.…”

Section: Non Parametric or Randomforest Nearest Neighbour Predictionmentioning

confidence: 99%

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

Penner¹,

Woods

Pitt

2015

Forests

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Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area-and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m 2 ).

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yaImpute: AnRPackage forkNN Imputation

Cited by 326 publications

References 18 publications

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

A Comparison of Selected Parametric and Non-Parametric Imputation Methods for Estimating Forest Biomass and Basal Area

Multiple-scale prediction of forest loss risk across Borneo

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

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